Open AccessDOI
Identification of stochastic time-varying systems
Kamal A. F. Moustafa
- Vol. 130, Iss: 4, pp 137-142
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TLDR
The parameter identification of a class of time-varying stochastic systems is considered and conditions which guarantee either almost sure convergence of the estimation error to the null or bounded mean-square error are obtained.Abstract:
The parameter identification of a class of time-varying stochastic systems is considered in this paper. Online schemes which track the time-varying parameters in real time are proposed. Conditions which guarantee either almost sure convergence of the estimation error to the null or bounded mean-square error are obtained. The analysis is based on stochastic Lyapunov functions. This allows the convergence conditions to be weakened, and makes investigation of the stability problem of the proposed identification schemes possible.read more
Citations
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Performance bounds of forgetting factor least-squares algorithms for time-varying systems with finite measurement data
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TL;DR: For time-varying systems, the properties of the well-known forgetting factor least-squares algorithm are studied in detail in the stochastic framework, and upperbounds and lowerbounds of the parameter estimation errors (PEE) are derived using directly the finite input-output data.
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Theory and Application of Adaptive Control
TL;DR: This survey of adaptive control theory and its applications reviews the progress during the years 1980 till 1984 and shows that carefully designed adaptive control systems have been used successfully in a broad variety of application areas.
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Identification of linear periodically time-varying systems using white-noise test inputs
A. D. Sams,Vasilis Z. Marmarelis +1 more
TL;DR: The simulation results corroborate the ability of this technique to practically identify periodically time-varying systems (of unknown periodicity) by use of a single input-output data-record.
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A variable forgetting factor RLS algorithm with application to fuzzy time-varying systems identification
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TL;DR: A modified RLS-type adaptive algorithm with variable forgetting factor, allowing the adaptive algorithm to track changes in the system automatically as well as produce a small steady-slate error, is introduced.
Proceedings ArticleDOI
Convergence of forgetting factor least square algorithms
Feng Ding,Tao Ding +1 more
TL;DR: Convergence of the forgetting factor least square (FFLS) algorithm is analyzed by using stochastic process theory; and the upper bound of the parameter estimation error is derived.
References
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Book
Stochastic Stability and Control
TL;DR: In this article, a book on stochastic stability and control dealing with Liapunov function approach to study of Markov processes is presented, which is based on the work of this article.
Book
System Identification Parameter and State Estimation
TL;DR: In this paper, system identification: parameter and state estimation, System identification: parametric estimation, parameter estimation: parameter estimation, state estimation: state estimation and identification of parameters, system identification, parameter identification and state identification.
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